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LatestCohere1.0
CohereCode

Cohere Releases North-Mini-Code, an Open MoE Model

The new Apache 2.0-licensed model is designed for code generation and agentic chat applications, using a Mixture-of-Experts architecture for efficiency.

Jun 5, 2026
NotableApache 2.0
North-Mini-Code 1.0

Cohere has introduced North-Mini-Code 1.0, a new open-source model aimed at developers working on coding and AI agent applications. The model is released under the permissive Apache 2.0 license, allowing for broad commercial and research use.

What sets North-Mini-Code apart is its Mixture-of-Experts (MoE) architecture. This design allows the model to activate only specific parts of its network—the "experts"—for any given task. The result is a model that can perform like a much larger dense model while requiring significantly less computational power during inference, making it more efficient to run.

According to the official release information available on its Hugging Face repository, the model is optimized for two primary use cases: direct code generation and powering "agentic chat." This suggests capabilities beyond simple code completion, enabling more complex conversational agents that can understand and execute programming-related instructions.

This release adds a noteworthy and efficient option to the landscape of open-source code models. By combining a commercially friendly license with the performance benefits of an MoE architecture, Cohere is providing a valuable tool for developers looking to build and deploy sophisticated, code-aware AI features without relying on proprietary, closed-source APIs.

Sources

  • CohereLabs/North-Mini-Code-1.0

    Hugging Face

    Visit
OlderBoson AI's Higgs Audio v3 Offers Expressive, Multilingual TTSBosonai · Text → Speech · 5 months agoNewerPaddleOCR's PP-OCRv6 Adds a Medium Detection ModelBaidu · Vision-Language · 5 months ago

Get the model

Hugging Face

Specs

Active params3B active
Context window500K tokens
Size61 GB
PrecisionBF16
ArchitectureCohere2MoeForCausalLM
LicenseAPACHE-2.0
Downloads8.1K
Likes582

Can you run it?

Runs on a 24 GB GPU at 4-bit.

  • BF16 (as published)

    80 GB GPU (A100 / H100) · 128 GB Mac Studio

    62.8 GB
  • 8-bit

    48 GB GPU (RTX 6000) or 2×24 GB · 64 GB Mac

    34.2 GB
  • 4-bit

    24 GB GPU (RTX 3090 / 4090) · 32 GB Mac

    20.1 GB
Your machine
GGUF builds
  • Mixture-of-experts: all 30.5B parameters must be resident even though only 3B are active per token — active count affects speed, not memory.
  • KV cache sized for 8,192 tokens of context; longer prompts need proportionally more.

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

CodeText / LLM

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